






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
							from transformers import TvltImageProcessor

if is_speech_available():
							from transformers import TvltFeatureExtractor

from transformers import TvltProcessor


@require_torch
class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							def lowerCAmelCase_			(  self:   Tuple    )		->		int:
											snake_case__						=  'ZinengTang/tvlt-base'
											snake_case__						=  tempfile.mkdtemp()
							def lowerCAmelCase_			(  self:   Optional[Any]							,						**UpperCamelCase:   Optional[Any]    )		->		List[Any]:
											return TvltImageProcessor.from_pretrained(self.checkpoint							,						**UpperCAmelCase_    )
							def lowerCAmelCase_			(  self:   str							,						**UpperCamelCase:   Optional[Any]    )		->		Tuple:
											return TvltFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase_    )
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Dict:
											shutil.rmtree(self.tmpdirname    )
							def lowerCAmelCase_			(  self:   Tuple    )		->		List[str]:
											snake_case__						=  self.get_image_processor()
											snake_case__						=  self.get_feature_extractor()

											snake_case__						=  TvltProcessor(image_processor=UpperCAmelCase_							,						feature_extractor=UpperCAmelCase_    )
											processor.save_pretrained(self.tmpdirname    )
											snake_case__						=  TvltProcessor.from_pretrained(self.tmpdirname    )

											self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase_    )
											self.assertIsInstance(processor.image_processor							,						UpperCAmelCase_    )
							def lowerCAmelCase_			(  self:   Tuple    )		->		Any:
											snake_case__						=  self.get_image_processor()
											snake_case__						=  self.get_feature_extractor()

											snake_case__						=  TvltProcessor(image_processor=UpperCAmelCase_							,						feature_extractor=UpperCAmelCase_    )

											snake_case__						=  np.ones([1_20_00]    )

											snake_case__						=  feature_extractor(UpperCAmelCase_							,						return_tensors='np'    )
											snake_case__						=  processor(audio=UpperCAmelCase_							,						return_tensors='np'    )

											for key in audio_dict.keys():
															self.assertAlmostEqual(audio_dict[key].sum()							,						input_processor[key].sum()							,						delta=1e-2    )
							def lowerCAmelCase_			(  self:   Any    )		->		int:
											snake_case__						=  self.get_image_processor()
											snake_case__						=  self.get_feature_extractor()

											snake_case__						=  TvltProcessor(image_processor=UpperCAmelCase_							,						feature_extractor=UpperCAmelCase_    )

											snake_case__						=  np.ones([3, 2_24, 2_24]    )

											snake_case__						=  image_processor(UpperCAmelCase_							,						return_tensors='np'    )
											snake_case__						=  processor(images=UpperCAmelCase_							,						return_tensors='np'    )

											for key in image_dict.keys():
															self.assertAlmostEqual(image_dict[key].sum()							,						input_processor[key].sum()							,						delta=1e-2    )
							def lowerCAmelCase_			(  self:   Dict    )		->		Dict:
											snake_case__						=  self.get_image_processor()
											snake_case__						=  self.get_feature_extractor()

											snake_case__						=  TvltProcessor(image_processor=UpperCAmelCase_							,						feature_extractor=UpperCAmelCase_    )

											snake_case__						=  np.ones([1_20_00]    )
											snake_case__						=  np.ones([3, 2_24, 2_24]    )

											snake_case__						=  processor(audio=UpperCAmelCase_							,						images=UpperCAmelCase_    )

											self.assertListEqual(list(inputs.keys()    )							,						['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask']    )

											# test if it raises when no input is passed
											with pytest.raises(UpperCAmelCase_    ):
															processor()


							def lowerCAmelCase_			(  self:   int    )		->		int:
											snake_case__						=  self.get_image_processor()
											snake_case__						=  self.get_feature_extractor()

											snake_case__						=  TvltProcessor(image_processor=UpperCAmelCase_							,						feature_extractor=UpperCAmelCase_    )

											self.assertListEqual(
											    processor.model_input_names							,						image_processor.model_input_names + feature_extractor.model_input_names							,						msg='`processor` and `image_processor`+`feature_extractor` model input names do not match'							,						)






"""simple docstring"""


import numpy as np



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_ = 1E-12		,   UpperCAmelCase_ = 1_00		,   )->				tuple[float, np.ndarray]:







       """simple docstring"""
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase_					) == np.iscomplexobj(UpperCAmelCase_					)
       UpperCamelCase       			=		np.iscomplexobj(UpperCAmelCase_					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase_		,   input_matrix.conj().T					)

       # Set convergence to False. Will define convergence when we exceed max_iterations
       # or when we have small changes from one iteration to next.

       UpperCamelCase       			=		False
       UpperCamelCase       			=		0
       UpperCamelCase       			=		0
       UpperCamelCase       			=		1E12

       while not convergence:
              # Multiple matrix by the vector.
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)
              # Normalize the resulting output vector.
              UpperCamelCase       			=		w / np.linalg.norm(UpperCAmelCase_					)
              # Find rayleigh quotient
              # (faster than usual b/c we know vector is normalized already)
              UpperCamelCase       			=		vector.conj().T if is_complex else vector.T
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              # Check convergence.
              UpperCamelCase       			=		np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     UpperCamelCase       			=		True

              UpperCamelCase       			=		lambda_

       if is_complex:
              UpperCamelCase       			=		np.real(lambda_					)

       return lambda_, vector



def 						lowerCamelCase__      (				)->				None:







       """simple docstring"""
       UpperCamelCase       			=		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       UpperCamelCase       			=		np.array([41, 4, 20]					)
       UpperCamelCase       			=		real_input_matrix.astype(np.complexaaa					)
       UpperCamelCase       			=		np.triu(1j * complex_input_matrix		,   1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       UpperCamelCase       			=		np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     UpperCamelCase       			=		real_input_matrix
                     UpperCamelCase       			=		real_vector
              elif problem_type == "complex":
                     UpperCamelCase       			=		complex_input_matrix
                     UpperCamelCase       			=		complex_vector

              # Our implementation.
              UpperCamelCase			,							UpperCamelCase       			=		power_iteration(UpperCAmelCase_		,   UpperCAmelCase_					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              UpperCamelCase			,							UpperCamelCase       			=		np.linalg.eigh(UpperCAmelCase_					)
              # Last eigenvalue is the maximum one.
              UpperCamelCase       			=		eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              UpperCamelCase       			=		eigen_vectors[:, -1]

              # Check our implementation and numpy gives close answers.
              assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
              # Take absolute values element wise of each eigenvector.
              # as they are only unique to a minus sign.
              assert np.linalg.norm(np.abs(UpperCAmelCase_					) - np.abs(UpperCAmelCase_					)					) <= 1E-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()





